GSA-SNP

GSA-SNP performs gene set analysis (GSA) on genome-wide association (GWA) data to detect coordinated associations among genes sharing biological functions and thereby increase power to identify genetic contributors to complex traits.

Key Features:

• Multiple GSA methods: Implements three widely used gene set analysis methods to provide methodological flexibility and robustness.
• Gene-set level aggregation: Aggregates marker-level data at the gene set level to address the large number of genetic markers tested in GWA studies.
• Enhanced analytical power: Focuses on collective associations within gene sets to improve detection of coordinated genetic effects and complex interactions.
• Integration with GWA studies: Provides a general approach for incorporating GSA into genome-wide association analyses to reveal biologically meaningful patterns.

Scientific Applications:

• Complex trait genetics: Identifies gene sets contributing collectively to phenotypic variation in complex traits, exemplified by analyses of adult height in a Korean population.
• Multifactorial disease research: Elucidates the genetic architecture of multifactorial diseases by detecting coordinated associations among functionally related genes.
• Interpretation of GWA results: Improves interpretability of genome-wide association findings by highlighting coordinated biological signals at the gene-set level.

Methodology:

Implements three GSA methods and aggregates marker-level statistics to the gene set level as a general approach for integrating gene set analysis into GWA studies.